Improving Reading Comprehension of First Year Engineering Students: A Quantitative Study at QUEST, Nawabshah, Pakistan
Bibliographic record
Abstract
This paper reports the results of the research conducted to explore whether students learn reading comprehension more successfully using the different approaches based on strategies in reading texts. The study was conducted at QUEST, in Pakistan and the respondents were selected from four engineering departments. Data was collected through a set of questionnaire used as the qualitative instrument among 311 respondents. However, Questionnaire data was analyzed by using SPSS 17. Descriptive statistics were used to analyze research variables for producing the Percentages, Mean and Standard Deviation of the data. The findings of this study reported that this research investigated 18 categories of reading comprehension. The highest mean score in reading comprehension was for “read aloud practices” category (=2.40) rated by all respondents; while the mean score for “asking questions before, during, and after reading” (= 1.48) was the lowest. However, no category of reading comprehension fell into low level of usage. In short, results, discussion and recommendations are presented for developing effective reading strategies to design syllabus for the engineering students to improve their reading proficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".